#initalize the parameters of the algorithm
parameters <- list()

#set the number of clusters
parameters$cluster_count <- 2

#initialize the kernel
K <- ?? #should be an N x N matrix containing similarity values between samples

#perform training
state <- kkmeans_train(K, parameters)

#display the clustering
print(state$clustering)
